





'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)










'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					lowerCAmelCase									=   """platform"""

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)






def   __A    (				a_    :	Dict   ,a_    :	List[Any]   ,a_    :	List[str]=None   ,a_    :	Optional[int]=None   ,a_    :	Any=None   ,a_    :	Any=None   ,a_    :	str=None   ,a_    :	Union[str, Any]=None   ,):
		if attention_mask is None:
				lowerCAmelCase  :     List[Any]       						= np.where(input_ids != config.pad_token_id   ,1   ,0						)
		if decoder_attention_mask is None:
				lowerCAmelCase  :     Union[str, Any]       						= np.where(decoder_input_ids != config.pad_token_id   ,1   ,0						)
		if head_mask is None:
				lowerCAmelCase  :     Dict       						= np.ones((config.encoder_layers, config.encoder_attention_heads)						)
		if decoder_head_mask is None:
				lowerCAmelCase  :     int       						= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
		if cross_attn_head_mask is None:
				lowerCAmelCase  :     Union[str, Any]       						= np.ones((config.decoder_layers, config.decoder_attention_heads)						)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_=13		,   a_=7		,   a_=True		,   a_=False		,   a_=99		,   a_=16		,   a_=2		,   a_=4		,   a_=4		,   a_="gelu"		,   a_=0.1		,   a_=0.1		,   a_=32		,   a_=2		,   a_=1		,   a_=0		,   a_=0.02		,   ):
			lowerCAmelCase  :     Optional[int]       						= parent
			lowerCAmelCase  :     Tuple       						= batch_size
			lowerCAmelCase  :     Any       						= seq_length
			lowerCAmelCase  :     int       						= is_training
			lowerCAmelCase  :     int       						= use_labels
			lowerCAmelCase  :     Any       						= vocab_size
			lowerCAmelCase  :     Tuple       						= hidden_size
			lowerCAmelCase  :     Optional[int]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     int       						= intermediate_size
			lowerCAmelCase  :     str       						= hidden_act
			lowerCAmelCase  :     List[Any]       						= hidden_dropout_prob
			lowerCAmelCase  :     List[str]       						= attention_probs_dropout_prob
			lowerCAmelCase  :     Dict       						= max_position_embeddings
			lowerCAmelCase  :     Optional[int]       						= eos_token_id
			lowerCAmelCase  :     Union[str, Any]       						= pad_token_id
			lowerCAmelCase  :     Tuple       						= bos_token_id
			lowerCAmelCase  :     Tuple       						= initializer_range



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,   self.vocab_size						)		,   3		,   self.vocab_size						)
			lowerCAmelCase  :     str       						= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,   dtype=np.intaa						))		,   -1						)

			lowerCAmelCase  :     List[str]       						= shift_tokens_right(a_		,   1		,   2						)

			lowerCAmelCase  :     Union[str, Any]       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=self.hidden_size		,   encoder_layers=self.num_hidden_layers		,   decoder_layers=self.num_hidden_layers		,   encoder_attention_heads=self.num_attention_heads		,   decoder_attention_heads=self.num_attention_heads		,   encoder_ffn_dim=self.intermediate_size		,   decoder_ffn_dim=self.intermediate_size		,   dropout=self.hidden_dropout_prob		,   attention_dropout=self.attention_probs_dropout_prob		,   max_position_embeddings=self.max_position_embeddings		,   eos_token_id=self.eos_token_id		,   bos_token_id=self.bos_token_id		,   pad_token_id=self.pad_token_id		,   initializer_range=self.initializer_range		,   use_cache=a_		,   )
			lowerCAmelCase  :     Tuple       						= prepare_blenderbot_inputs_dict(a_		,   a_		,   a_						)
			return config, inputs_dict



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= self.prepare_config_and_inputs()
			return config, inputs_dict



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Any       						= 20
			lowerCAmelCase  :     int       						= model_class_name(a_						)

			lowerCAmelCase  :     Tuple       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     Tuple       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype="i4"						)

			lowerCAmelCase  :     Dict       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )
			lowerCAmelCase  :     Union[str, Any]       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     Tuple       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Optional[Any]       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     int       						= model.decode(a_		,   a_						)

			lowerCAmelCase  :     List[str]       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= 20
			lowerCAmelCase  :     Tuple       						= model_class_name(a_						)

			lowerCAmelCase  :     Dict       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     str       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     Any       						= jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
			    ]		,   axis=-1		,   )

			lowerCAmelCase  :     Union[str, Any]       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )

			lowerCAmelCase  :     Any       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )
			lowerCAmelCase  :     int       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Any       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_attention_mask=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     List[str]       						= model.decode(a_		,   a_		,   decoder_attention_mask=a_						)

			lowerCAmelCase  :     int       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)






@require_flax
class 		lowerCamelCase	(   unittest.TestCase							):
	snake_case_   = 99



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]		,   dtype=np.intaa		,   )

			lowerCAmelCase  :     Union[str, Any]       						= input_ids.shape[0]
			lowerCAmelCase  :     Dict       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=24		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=32		,   decoder_ffn_dim=32		,   max_position_embeddings=48		,   eos_token_id=2		,   pad_token_id=1		,   bos_token_id=0		,   )
			return config, input_ids, batch_size



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self._get_config_and_data()
			lowerCAmelCase  :     Tuple       						= FlaxBlenderbotForConditionalGeneration(a_						)
			lowerCAmelCase  :     str       						= lm_model(input_ids=a_						)
			lowerCAmelCase  :     Tuple       						= (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs["logits"].shape		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= BlenderbotConfig(
			    vocab_size=self.vocab_size		,   d_model=14		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=8		,   decoder_ffn_dim=8		,   max_position_embeddings=48		,   )
			lowerCAmelCase  :     int       						= FlaxBlenderbotForConditionalGeneration(a_						)
			lowerCAmelCase  :     Optional[Any]       						= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     List[str]       						= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     List[str]       						= lm_model(input_ids=a_		,   decoder_input_ids=a_						)
			lowerCAmelCase  :     List[str]       						= (*summary.shape, config.vocab_size)
			self.assertEqual(outputs["logits"].shape		,   a_						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,   dtype=np.intaa						)
			lowerCAmelCase  :     Dict       						= shift_tokens_right(a_		,   1		,   2						)
			lowerCAmelCase  :     Optional[Any]       						= np.equal(a_		,   1						).astype(np.floataa						).sum()
			lowerCAmelCase  :     Any       						= np.equal(a_		,   1						).astype(np.floataa						).sum()
			self.assertEqual(shifted.shape		,   input_ids.shape						)
			self.assertEqual(a_		,   n_pad_before - 1						)
			self.assertTrue(np.equal(shifted[:, 0]		,   2						).all()						)






@require_flax
class 		lowerCamelCase	(   _A   , unittest.TestCase   , _A							):
	snake_case_   = True
	snake_case_   = (
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	snake_case_   = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= FlaxBlenderbotModelTester(self						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     List[str]       						= self._prepare_for_class(a_		,   a_						)
							lowerCAmelCase  :     str       						= model_class(a_						)

							@jax.jit
							def encode_jitted(a_		,   a_=None		,   **a_						):
									return model.encode(input_ids=a_		,   attention_mask=a_						)

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     List[Any]       						= encode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     Tuple       						= encode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     int       						= model_class(a_						)
							lowerCAmelCase  :     Optional[int]       						= model.encode(inputs_dict["input_ids"]		,   inputs_dict["attention_mask"]						)

							lowerCAmelCase  :     Optional[Any]       						= {
							    "decoder_input_ids": inputs_dict["decoder_input_ids"],
							    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
							    "encoder_outputs": encoder_outputs,
							}

							@jax.jit
							def decode_jitted(a_		,   a_		,   a_						):
									return model.decode(
									    decoder_input_ids=a_		,   decoder_attention_mask=a_		,   encoder_outputs=a_		,   )

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     str       						= decode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     Optional[Any]       						= decode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	@slow
	def 	_lowerCamelCase     ( self						):
			for model_class_name in self.all_model_classes:
					lowerCAmelCase  :     List[str]       						= model_class_name.from_pretrained("facebook/blenderbot-400M-distill"						)
					# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
					lowerCAmelCase  :     Union[str, Any]       						= np.ones((1, 1)						) * model.config.eos_token_id
					lowerCAmelCase  :     Dict       						= model(a_						)
					self.assertIsNotNone(a_						)





	@unittest.skipUnless(jax_device != "cpu"		,   "3B test too slow on CPU."						)
	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
			lowerCAmelCase  :     Any       						= {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

			lowerCAmelCase  :     Optional[int]       						= FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"		,   from_pt=a_						)
			lowerCAmelCase  :     List[str]       						= BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"						)

			lowerCAmelCase  :     Any       						= ["Sam"]
			lowerCAmelCase  :     int       						= tokenizer(a_		,   return_tensors="jax"						)

			lowerCAmelCase  :     List[Any]       						= model.generate(**a_		,   **a_						)
			lowerCAmelCase  :     Optional[Any]       						= "Sam is a great name. It means \"sun\" in Gaelic."

			lowerCAmelCase  :     Optional[Any]       						= tokenizer.batch_decode(a_		,   **a_						)
			assert generated_txt[0].strip() == tgt_text



